Multiscale Gaussian Random Fields for Cosmological Simulations

This paper describes the generation of initial conditions for numerical simulations in cosmology with multiple levels of resolution, or multiscale simulations. We present the theory of adaptive mesh refinement of Gaussian random fields followed by the implementation and testing of a computer code package performing this refinement called GRAFIC2. This package is available to the computational cosmology community at http://arcturus.mit.edu/grafic/ or by email from the author.Comment: 38 pages. Submitted to ApJ Supplemen

Dependence of the Inner DM Profile on the Halo Mass

I compare the density profile of dark matter (DM) halos in cold dark matter (CDM) N-body simulations with 1 Mpc, 32 Mpc, 256 Mpc and 1024 Mpc box sizes. In dimensionless units the simulations differ only for the initial power spectrum of density perturbations. I compare the profiles when the most massive halos are composed of about 10^5 DM particles. The DM density profiles of the halos in the 1 Mpc box show systematically shallower cores with respect to the corresponding halos in the 32 Mpc simulation that have masses, M_{dm}, typical of the Milky Way and are fitted by a NFW profile. The DM density profiles of the halos in the 256 Mpc box are consistent with having steeper cores than the corresponding halos in the 32 Mpc simulation, but higher mass resolution simulations are needed to strengthen this result. Combined, these results indicate that the density profile of DM halos is not universal, presenting shallower cores in dwarf galaxies and steeper cores in clusters. Physically the result sustains the hypothesis that the mass function of the accreting satellites determines the inner slope of the DM profile. In comoving coordinates, r, the profile \rho_{dm} \propto 1/(X^\alpha(1+X)^{3-\alpha}), with X=c_\Delta r/r_\Delta, r_\Delta is the virial radius and \alpha =\alpha(M_{dm}), provides a good fit to all the DM halos from dwarf galaxies to clusters at any redshift with the same concentration parameter c_\Delta ~ 7. The slope, \gamma, of the outer parts of the halo appears to depend on the acceleration of the universe: when the scale parameter is a=(1+z)^{-1} < 1, the slope is \gamma ~ 3 as in the NFW profile, but \gamma ~ 4 at a > 1 when \Omega_\Lambda ~ 1 and the universe is inflating.[abridged]Comment: Accepted for publication in MNRAS. 13 pages, including 11 figures and 2 tables. The revised version has an additional discussion section and work on the velocity dispersion anisotrop

Star Formation in a Cosmological Simulation of Reionization

We study the luminosity functions of high-redshift galaxies in detailed hydrodynamic simulations of cosmic reionization, which are designed to reproduce the evolution of the Lyman-alpha forest between z=5 and z=6. We find that the luminosity functions and total stellar mass densities are in agreement with observations when plausible assumptions about reddenning at z=6 are made. Our simulations support the conclusion that stars alone reionized the universe.Comment: Accepted for publication in Ap

Knockouts, Robustness and Cell Cycles

The response to a knockout of a node is a characteristic feature of a networked dynamical system. Knockout resilience in the dynamics of the remaining nodes is a sign of robustness. Here we study the effect of knockouts for binary state sequences and their implementations in terms of Boolean threshold networks. Beside random sequences with biologically plausible constraints, we analyze the cell cycle sequence of the species Saccharomyces cerevisiae and the Boolean networks implementing it. Comparing with an appropriate null model we do not find evidence that the yeast wildtype network is optimized for high knockout resilience. Our notion of knockout resilience weakly correlates with the size of the basin of attraction, which has also been considered a measure of robustness.Comment: 11 pages, 3 figures, 3 table

Quantifying structure in networks

We investigate exponential families of random graph distributions as a framework for systematic quantification of structure in networks. In this paper we restrict ourselves to undirected unlabeled graphs. For these graphs, the counts of subgraphs with no more than k links are a sufficient statistics for the exponential families of graphs with interactions between at most k links. In this framework we investigate the dependencies between several observables commonly used to quantify structure in networks, such as the degree distribution, cluster and assortativity coefficients.Comment: 17 pages, 3 figure

Neuronal avalanches recorded in the awake and sleeping monkey do not show a power law but can be reproduced by a self-organized critical model

Poster presentation: Self-organized critical (SOC) systems are complex dynamical systems that may express cascades of events, called avalanches [1]. The SOC state was proposed to govern brain function, because of its activity fluctuations over many orders of magnitude, its sensitivity to small input and its long term stability [2,3]. In addition, the critical state is optimal for information storage and processing [4]. Both hallmark features of SOC systems, a power law distribution f(s) for the avalanche size s and a branching parameter (bp) of unity, were found for neuronal avalanches recorded in vitro [5]. However, recordings in vivo yielded contradictory results [6]. Electrophysiological recordings in vivo only cover a small fraction of the brain, while criticality analysis assumes that the complete system is sampled. We hypothesized that spatial subsampling might influence the observed avalanche statistics. In addition, SOC models can have different connectivity, but always show a power law for f(s) and bp = 1 when fully sampled. This may not be the case under subsampling, however. Here, we wanted to know whether a state change from awake to asleep could be modeled by changing the connectivity of a SOC model without leaving the critical state. We simulated a SOC model [1] and calculated f(s) and bp obtained from sampling only the activity of a set of 4 × 4 sites, representing the electrode positions in the cortex. We compared these results with results obtained from multielectrode recordings of local field potentials (LFP) in the cortex of behaving monkeys. We calculated f(s) and bp for the LFP activity recorded while the monkey was either awake or asleep and compared these results to results obtained from two subsampled SOC model with different connectivity. f(s) and bp were very similar for both the experiments and the subsampled SOC model, but in contrast to the fully sampled model, f(s) did not show a power law and bp was smaller than unity. With increasing the distance between the sampling sites, f(s) changed from "apparently supercritical" to "apparently subcritical" distributions in both the model and the LFP data. f(s) and bp calculated from LFP recorded during awake and asleep differed. These changes could be explained by altering the connectivity in the SOC model. Our results show that subsampling can prevent the observation of the characteristic power law and bp in SOC systems, and misclassifications of critical systems as sub- or supercritical are possible. In addition, a change in f(s) and bp for different states (awake/asleep) does not necessarily imply a change from criticality to sub- or supercriticality, but can also be explained by a change in the effective connectivity of the network without leaving the critical state

Correlation in states of two identical particles

We identify the correlation in a state of two identical particles as the residual information beyond what is already contained in the 1-particle reduced density matrix, and propose a correlation measure based on the maximum entropy principle. We obtain the analytical results of the correlation measure, which make it computable for arbitrary two-particle states. We also show that the degrees of correlation in the same two-particle states with different particle types will decrease in the following order: bosons, fermions, and distinguishable particles.Comment: 3.6 page

Measuring the Nonlinear Biasing Function from a Galaxy Redshift Survey

We present a simple method for evaluating the nonlinear biasing function of galaxies from a redshift survey. The nonlinear biasing is characterized by the conditional mean of the galaxy density fluctuation given the underlying mass density fluctuation, or by the associated parameters of mean biasing and nonlinearity (following Dekel & Lahav 1999). Using the distribution of galaxies in cosmological simulations, at smoothing of a few Mpc, we find that the mean biasing can be recovered to a good accuracy from the cumulative distribution functions (CDFs) of galaxies and mass, despite the biasing scatter. Then, using a suite of simulations of different cosmological models, we demonstrate that the matter CDF is robust compared to the difference between it and the galaxy CDF, and can be approximated for our purpose by a cumulative log-normal distribution of 1+\delta with a single parameter \sigma. Finally, we show how the nonlinear biasing function can be obtained with adequate accuracy directly from the observed galaxy CDF in redshift space. Thus, the biasing function can be obtained from counts in cells once the rms mass fluctuation at the appropriate scale is assumed a priori. The relative biasing function between different galaxy types is measurable in a similar way. The main source of error is sparse sampling, which requires that the mean galaxy separation be smaller than the smoothing scale. Once applied to redshift surveys such as PSCz, 2dF, SDSS, or DEEP, the biasing function can provide valuable constraints on galaxy formation and structure evolution.Comment: 23 pages, 7 figures, revised version, accepted for publication in Ap

A new approach to cosmological perturbations in f(R) models

We propose an analytic procedure that allows to determine quantitatively the deviation in the behavior of cosmological perturbations between a given f(R) modified gravity model and a LCDM reference model. Our method allows to study structure formation in these models from the largest scales, of the order of the Hubble horizon, down to scales deeply inside the Hubble radius, without employing the so-called "quasi-static" approximation. Although we restrict our analysis here to linear perturbations, our technique is completely general and can be extended to any perturbative order.Comment: 21 pages, 2 figures; Revised version according to reviewer's suggestions; Typos corrected; Added Reference

The Halo Mass Function: High-Redshift Evolution and Universality

We study the formation of dark matter halos in the concordance LCDM model over a wide range of redshifts, from z=20 to the present. Our primary focus is the halo mass function, a key probe of cosmology. By performing a large suite of nested-box N-body simulations with careful convergence and error controls (60 simulations with box sizes from 4 to 256 Mpc/h, we determine the mass function and its evolution with excellent statistical and systematic errors, reaching a few percent over most of the considered redshift and mass range. Across the studied redshifts, the halo mass is probed over 6 orders of magnitude (10^7 - 10^13.5 M_sun/h). Historically, there has been considerable variation in the high redshift mass function as obtained by different groups. We have made a concerted effort to identify and correct possible systematic errors in computing the mass function at high redshift and to explain the discrepancies between some of the previous results. We discuss convergence criteria for the required force resolution, simulation box size, halo mass range, initial and final redshift, and time stepping. Because of conservative cuts on the mass range probed by individual boxes, our results are relatively insensitive to simulation volume, the remaining sensitivity being consistent with extended Press-Schechter theory. Previously obtained mass function fits near z=0, when scaled by linear theory, are in good agreement with our results at all redshifts, although a mild redshift dependence consistent with that found by Reed and collaborators exists at low redshifts.Comment: 20 pages, 15 figures. Minor changes to the text and figures; results and conclusions unchange